{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T15:32:40Z","timestamp":1766503960228,"version":"3.44.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NIH"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>We developed a rapid scanning optical microscope, termed \u201cBlurryScope\u201d, that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point and smaller size\/weight. Using BlurryScope, we implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on motion-blurred images of immunohistochemically (IHC) stained breast tissue sections, achieving concordant results with those obtained from a high-end digital scanning microscope. Using a test set of 284 unique patient cores, we achieved testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0\/1+, 2+\/3+) HER2 classification, respectively. BlurryScope automates the entire workflow, from image scanning to stitching and cropping, as well as HER2 score classification.<\/jats:p>","DOI":"10.1038\/s41746-025-01882-x","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T23:34:27Z","timestamp":1754436867000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images"],"prefix":"10.1038","volume":"8","author":[{"given":"Michael John","family":"Fanous","sequence":"first","affiliation":[]},{"given":"Christopher Michael","family":"Seybold","sequence":"additional","affiliation":[]},{"given":"Hanlong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Nir","family":"Pillar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0717-683X","authenticated-orcid":false,"given":"Aydogan","family":"Ozcan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"1882_CR1","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.3390\/jcm9113697","volume":"9","author":"SW Jahn","year":"2020","unstructured":"Jahn, S. W., Plass, M. & Moinfar, F. Digital pathology: advantages, limitations and emerging perspectives. J. Clin. Med. 9, 3697 (2020).","journal-title":"J. Clin. Med."},{"key":"1882_CR2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.4103\/jpi.jpi_94_20","volume":"12","author":"S Rajaganesan","year":"2021","unstructured":"Rajaganesan, S. et al. Comparative assessment of digital pathology systems for primary diagnosis. J. Pathol. Inform. 12, 25 (2021).","journal-title":"J. Pathol. Inform."},{"key":"1882_CR3","doi-asserted-by":"publisher","first-page":"116650","DOI":"10.1016\/j.socscimed.2024.116650","volume":"345","author":"O Kusta","year":"2024","unstructured":"Kusta, O. et al. Speed, accuracy, and efficiency: The promises and practices of digitization in pathology. Soc. Sci. Med. 345, 116650 (2024).","journal-title":"Soc. Sci. Med."},{"key":"1882_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ymeth.2014.06.015","volume":"70","author":"PW Hamilton","year":"2014","unstructured":"Hamilton, P. W. et al. Digital pathology and image analysis in tissue biomarker research. Methods 70, 59\u201373 (2014).","journal-title":"Methods"},{"key":"1882_CR5","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1364\/BOE.11.000281","volume":"11","author":"ZF Phillips","year":"2020","unstructured":"Phillips, Z. F., Dean, S., Recht, B. & Waller, L. High-throughput fluorescence microscopy using multi-frame motion deblurring. Biomed. Opt. Express 11, 281\u2013300 (2020).","journal-title":"Biomed. Opt. Express"},{"key":"1882_CR6","doi-asserted-by":"publisher","first-page":"064011","DOI":"10.1117\/1.2402110","volume":"11","author":"R Wolleschensky","year":"2006","unstructured":"Wolleschensky, R., Zimmermann, B. & Kempe, M. High-speed confocal fluorescence imaging with a novel line scanning microscope. J. Biomed. Opt. 11, 064011\u2013064014 (2006).","journal-title":"J. Biomed. Opt."},{"key":"1882_CR7","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/TNB.2004.837899","volume":"3","author":"P Prabhat","year":"2004","unstructured":"Prabhat, P., Ram, S., Ward, E. S. & Ober, R. J. Simultaneous imaging of different focal planes in fluorescence microscopy for the study of cellular dynamics in three dimensions. IEEE Trans. Nanobiosci. 3, 237\u2013242 (2004).","journal-title":"IEEE Trans. Nanobiosci."},{"key":"1882_CR8","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TED.2009.2030648","volume":"56","author":"G Lepage","year":"2009","unstructured":"Lepage, G., Bogaerts, J. & Meynants, G. Time-delay-integration architectures in CMOS image sensors. IEEE Trans. Electron Devices 56, 2524\u20132533 (2009).","journal-title":"IEEE Trans. Electron Devices"},{"key":"1882_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s13000-023-01352-6","volume":"18","author":"T Zehra","year":"2023","unstructured":"Zehra, T., Parwani, A., Abdul-Ghafar, J. & Ahmad, Z. A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world. Diagn. Pathol. 18, 68 (2023).","journal-title":"Diagn. Pathol."},{"key":"1882_CR10","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1038\/s41377-022-00999-y","volume":"11","author":"Y Rivenson","year":"2022","unstructured":"Rivenson, Y. & Ozcan, A. Deep learning accelerates whole slide imaging for next-generation digital pathology applications. Light11, 300 (2022).","journal-title":"Light"},{"key":"1882_CR11","first-page":"23","volume":"7","author":"N Farahani","year":"2015","unstructured":"Farahani, N., Parwani, A. V. & Pantanowitz, L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Lab. Med. Int. 7, 23\u201333 (2015).","journal-title":"Pathol. Lab. Med. Int."},{"key":"1882_CR12","doi-asserted-by":"publisher","first-page":"105276","DOI":"10.1016\/j.ebiom.2024.105276","volume":"107","author":"D Choudhury","year":"2024","unstructured":"Choudhury, D. et al. Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning. EBioMedicine 107, 105276 (2024).","journal-title":"EBioMedicine"},{"key":"1882_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41377-019-0129-y","volume":"8","author":"Y Rivenson","year":"2019","unstructured":"Rivenson, Y. et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light8, 23 (2019).","journal-title":"Light"},{"unstructured":"Bayramoglu, N., Kaakinen, M., Eklund, L. & Heikkila, J. in: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp 64\u201371.","key":"1882_CR14"},{"key":"1882_CR15","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1364\/OPTICA.4.001437","volume":"4","author":"Y Rivenson","year":"2017","unstructured":"Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437\u20131443 (2017).","journal-title":"Optica"},{"key":"1882_CR16","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.cell.2018.03.040","volume":"173","author":"EM Christiansen","year":"2018","unstructured":"Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792\u2013803.e719 (2018).","journal-title":"Cell"},{"key":"1882_CR17","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1038\/s41592-018-0111-2","volume":"15","author":"C Ounkomol","year":"2018","unstructured":"Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15, 917\u2013920 (2018).","journal-title":"Nat. Methods"},{"key":"1882_CR18","doi-asserted-by":"publisher","DOI":"10.1063\/5.0050889","volume":"6","author":"M Fanous","year":"2021","unstructured":"Fanous, M. et al. Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS). APL Photonics 6, 076103 (2021).","journal-title":"APL Photonics"},{"key":"1882_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20062-x","volume":"11","author":"ME Kandel","year":"2020","unstructured":"Kandel, M. E. et al. Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments. Nat. Commun. 11, 1\u201310 (2020).","journal-title":"Nat. Commun."},{"key":"1882_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41377-021-00620-8","volume":"10","author":"N Goswami","year":"2021","unstructured":"Goswami, N. et al. Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity. Light10, 1\u201312 (2021).","journal-title":"Light"},{"key":"1882_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-28214-x","volume":"13","author":"C Hu","year":"2022","unstructured":"Hu, C. et al. Live-dead assay on unlabeled cells using phase imaging with computational specificity. Nat. Commun. 13, 713 (2022).","journal-title":"Nat. Commun."},{"key":"1882_CR22","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1364\/OPTICA.6.000921","volume":"6","author":"G Barbastathis","year":"2019","unstructured":"Barbastathis, G., Ozcan, A. & Situ, G. On the use of deep learning for computational imaging. Optica 6, 921\u2013943 (2019).","journal-title":"Optica"},{"key":"1882_CR23","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1038\/s41377-024-01544-9","volume":"13","author":"MJ Fanous","year":"2024","unstructured":"Fanous, M. J. et al. Neural network-based processing and reconstruction of compromised biophotonic image data. Light13, 231 (2024).","journal-title":"Light"},{"key":"1882_CR24","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.1364\/BOE.5.002376","volume":"5","author":"L Tian","year":"2014","unstructured":"Tian, L., Li, X., Ramchandran, K. & Waller, L. Multiplexed coded illumination for Fourier Ptychography with an LED array microscope. Biomed. Opt. Express 5, 2376\u20132389 (2014).","journal-title":"Biomed. Opt. Express"},{"key":"1882_CR25","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1364\/OE.445001","volume":"30","author":"X Yao","year":"2022","unstructured":"Yao, X. et al. Increasing a microscope\u2019s effective field of view via overlapped imaging and machine learning. Opt. Express 30, 1745\u20131761 (2022).","journal-title":"Opt. Express"},{"key":"1882_CR26","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1021\/acsphotonics.8b00146","volume":"5","author":"Y Rivenson","year":"2018","unstructured":"Rivenson, Y. et al. Deep learning enhanced mobile-phone microscopy. ACS Photonics 5, 2354\u20132364 (2018).","journal-title":"ACS Photonics"},{"key":"1882_CR27","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1364\/OE.27.000644","volume":"27","author":"YF Cheng","year":"2019","unstructured":"Cheng, Y. F. et al. Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy. Opt. Express 27, 644\u2013656 (2019).","journal-title":"Opt. Express"},{"key":"1882_CR28","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41377-022-00952-z","volume":"11","author":"MJ Fanous","year":"2022","unstructured":"Fanous, M. J. & Popescu, G. GANscan: continuous scanning microscopy using deep learning deblurring. Light11, 265 (2022).","journal-title":"Light"},{"key":"1882_CR29","doi-asserted-by":"publisher","first-page":"2415","DOI":"10.1016\/S0140-6736(16)32417-5","volume":"389","author":"S Loibl","year":"2017","unstructured":"Loibl, S. & Gianni, L. HER2-positive breast cancer. Lancet 389, 2415\u20132429 (2017).","journal-title":"Lancet"},{"key":"1882_CR30","doi-asserted-by":"publisher","first-page":"632079","DOI":"10.3389\/fphar.2020.632079","volume":"11","author":"M Zubair","year":"2021","unstructured":"Zubair, M., Wang, S. & Ali, N. Advanced approaches to breast cancer classification and diagnosis. Front. Pharmacol. 11, 632079 (2021).","journal-title":"Front. Pharmacol."},{"key":"1882_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1159\/000325746","volume":"15","author":"K Goddard","year":"2011","unstructured":"Goddard, K. et al. HER2 evaluation and its impact on breast cancer treatment decisions. Public Health Genomics 15, 1\u201310 (2011).","journal-title":"Public Health Genomics"},{"key":"1882_CR32","first-page":"1","volume":"29","author":"H Chen","year":"2023","unstructured":"Chen, H., Huang, L., Liu, T. & Ozcan, A. eFIN: enhanced Fourier imager network for generalizable autofocusing and pixel super-resolution in holographic imaging. IEEE J. Sel. Top. Quantum Electron. 29, 1\u201310 (2023).","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"unstructured":"Diepeveen, A. The 10 Best Digital Pathology Scanners. https:\/\/lumeadigital.com\/the-10-best-digital-pathology-scanners\/ (2022).","key":"1882_CR33"},{"key":"1882_CR34","doi-asserted-by":"publisher","first-page":"0048","DOI":"10.34133\/bmef.0048","volume":"5","author":"SY Selcuk","year":"2024","unstructured":"Selcuk, S. Y. et al. Automated HER2 scoring in breast cancer images using deep learning and pyramid sampling. BME Front 5, 0048 (2024).","journal-title":"BME Front"},{"key":"1882_CR35","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1038\/s41377-021-00687-3","volume":"10","author":"X Chen","year":"2021","unstructured":"Chen, X. et al. Lipid droplets as endogenous intracellular microlenses. Light10, 242 (2021).","journal-title":"Light"},{"key":"1882_CR36","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1126\/science.aat8084","volume":"361","author":"X Lin","year":"2018","unstructured":"Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004\u20131008 (2018).","journal-title":"Science"},{"key":"1882_CR37","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adn9420","volume":"10","author":"B Bai","year":"2024","unstructured":"Bai, B. et al. Information-hiding cameras: Optical concealment of object information into ordinary images. Sci. Adv. 10, eadn9420 (2024).","journal-title":"Sci. Adv."},{"key":"1882_CR38","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adg1505","volume":"9","author":"J Li","year":"2023","unstructured":"Li, J. et al. Unidirectional imaging using deep learning\u2013designed materials. Sci. Adv. 9, eadg1505 (2023).","journal-title":"Sci. Adv."},{"key":"1882_CR39","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.add3433","volume":"8","author":"\u00c7 I\u015f\u0131l","year":"2022","unstructured":"I\u015f\u0131l, \u00c7. et al. Super-resolution image display using diffractive decoders. Sci. Adv. 8, eadd3433 (2022).","journal-title":"Sci. Adv."},{"key":"1882_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-20268-z","volume":"12","author":"M Veli","year":"2021","unstructured":"Veli, M. et al. Terahertz pulse shaping using diffractive surfaces. Nat. Commun. 12, 37 (2021).","journal-title":"Nat. Commun."},{"key":"1882_CR41","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-49304-y","volume":"15","author":"C-Y Shen","year":"2024","unstructured":"Shen, C.-Y. et al. All-optical phase conjugation using diffractive wavefront processing. Nat. Commun. 15, 4989 (2024).","journal-title":"Nat. Commun."},{"key":"1882_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-45982-w","volume":"15","author":"J Hu","year":"2024","unstructured":"Hu, J. et al. Diffractive optical computing in free space. Nat. Commun. 15, 1525 (2024).","journal-title":"Nat. Commun."},{"key":"1882_CR43","first-page":"1","volume":"29","author":"Y Li","year":"2022","unstructured":"Li, Y., Luo, Y., Bai, B. & Ozcan, A. Analysis of diffractive neural networks for seeing through random diffusers. IEEE J. Sel. Top. Quantum Electron. 29, 1\u201317 (2022).","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"key":"1882_CR44","doi-asserted-by":"publisher","first-page":"220048","DOI":"10.29026\/oea.2023.220048","volume":"6","author":"D Pirone","year":"2023","unstructured":"Pirone, D. et al. 3D imaging lipidometry in single cell by in-flow holographic tomography. Opto Electron. Adv. 6, 220048\u2013220041 (2023).","journal-title":"Opto Electron. Adv."},{"key":"1882_CR45","doi-asserted-by":"publisher","first-page":"230083-230081","DOI":"10.29026\/oea.2023.230083","volume":"6","author":"MJ Fanous","year":"2023","unstructured":"Fanous, M. J. & Ozcan, A. In-flow holographic tomography boosts lipid droplet quantification. Opto Electron. Adv. 6, 230083-230081\u2013230083-230083 (2023).","journal-title":"Opto Electron. Adv."},{"key":"1882_CR46","doi-asserted-by":"publisher","first-page":"e0241084","DOI":"10.1371\/journal.pone.0241084","volume":"15","author":"M Fanous","year":"2020","unstructured":"Fanous, M. et al. Quantifying myelin content in brain tissue using color spatial light interference microscopy (cSLIM). PLoS ONE 15, e0241084 (2020).","journal-title":"PLoS ONE"},{"unstructured":"Annamaa, A. in: Proceedings of the 15th Koli Calling Conference on Computing Education Research. pp 117\u2013121.","key":"1882_CR47"},{"key":"1882_CR48","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1038\/s41377-022-00949-8","volume":"11","author":"H Chen","year":"2022","unstructured":"Chen, H., Huang, L., Liu, T. & Ozcan, A. Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization. Light 11, 254 (2022).","journal-title":"Light"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01882-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01882-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01882-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T15:16:20Z","timestamp":1757344580000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01882-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1882"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01882-x","relation":{},"ISSN":["2398-6352"],"issn-type":[{"type":"electronic","value":"2398-6352"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"assertion":[{"value":"2 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.O., M.J.F., and C.M.S. have a pending patent application [63\/710,581] related to the AI-assisted scanning microscope developed in this work. A.O. and M.J.F. have a pending patent application [63\/768,477] on additional embodiments of the microscope. H.C. and N.P. declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"506"}}